Estimation of tidal volume using load cells on a hospital bed

Abstract

A method and apparatus for monitoring the respiration of a patient supported on a patient support apparatus through receiving signals from load cells supporting a patient on the patient support apparatus, processing the signals to characterize movement of the patient's center of mass, using the movement of the patient's center of mass, determine respiratory characteristic of the patient, and communicating the respiratory characteristic of the patient to a caregiver.

1 . A method of monitoring the respiration of a patient supported on a patient support apparatus comprising:
 receiving signals from load cells supporting a patient on the patient support apparatus;   processing the signals to characterize movement of the patient's center of mass;   using the movement of the patient's center of mass, determine an instantaneous tidal volume of the patient; and   communicating the instantaneous tidal volume of the patient to a caregiver.   
     
     
         2 . The method of  claim 1 , further comprising:
 using the movement of the patient's center of mass, determine an instantaneous respiration rate for the patient; and   communicating the instantaneous respiration rate of the patient to a caregiver.   
     
     
         3 . The method of  claim 2 , further comprising:
 comparing one or both of the instantaneous tidal volume and the instantaneous respiration rate to a pre-determined threshold and, if one or both of the values exceeds a respective predetermined limit, generating an alert to the caregiver.   
     
     
         4 . The method of  claim 3 , further comprising:
 training a model for the patient support apparatus including the features of the patient's ballistocardiographic heart rate, the patient weight, and movement of the patient's center of mass in three axes; and   when implementing the step of processing the signals to characterize movement of the patient's center of mass, applying the trained model to improve the characterization.   
     
     
         5 . The method of  claim 2 , further comprising:
 training a model for the patient support apparatus including the features of the patient's ballistocardiographic heart rate, the patient weight, and movement of the patient's center of mass in three axes; and   when implementing the step of processing the signals to characterize movement of the patient's center of mass, applying the trained model to improve the characterization.   
     
     
         6 . The method of  claim 2 , further comprising:
 training a model for the patient support apparatus including the feature of movement of the patient's rib cage in the dorso-ventral direction; and   when implementing the step of processing the signals to characterize movement of the patient's center of mass, applying the trained model to improve the characterization.   
     
     
         7 . The method of  claim 2 , further comprising:
 training a model for the patient support apparatus including the feature of movement of the patient's in the Z axis of the bed; and   when implementing the step of processing the signals to characterize movement of the patient's center of mass, applying the trained model to improve the characterization.   
     
     
         8 . The method of  claim 1 , further comprising:
 training a model for the patient support apparatus including the features of the patient's ballistocardiographic heart rate, the patient weight, and movement of the patient's center of mass in three axes; and   when implementing the step of processing the signals to characterize movement of the patient's center of mass, applying the trained model to improve the characterization.   
     
     
         9 . The method of  claim 1 , further comprising:
 training a model for the patient support apparatus including the feature of movement of the patient's rib cage in the dorso-ventral direction; and   when implementing the step of processing the signals to characterize movement of the patient's center of mass, applying the trained model to improve the characterization.   
     
     
         10 . The method of  claim 1 , further comprising:
 training a model for the patient support apparatus including the feature of movement of the patient's in the Z axis of the bed; and   when implementing the step of processing the signals to characterize movement of the patient's center of mass, applying the trained model to improve the characterization.   
     
     
         11 . A patient support apparatus comprising:
 a patient support frame;   a plurality of load cells supporting the patient support frame; and   a control system including a processor and a memory device, the memory device including instructions that, when executed by the processor, cause the processor to:   receive signals from the load cells;   process the signals to characterize movement of a patient's center of mass;   use the movement of the patient's center of mass, determine an instantaneous tidal volume of the patient; and   communicate the instantaneous tidal volume of the patient to a caregiver.   
     
     
         12 . The patient support apparatus of  claim 11 , wherein the memory device includes further instructions that, when executed by the processor, cause the processor to:
 use the movement of the patient's center of mass, determine an instantaneous respiration rate for the patient; and   communicate the instantaneous respiration rate of the patient to a caregiver.   
     
     
         13 . The patient support apparatus of  claim 12 , wherein the memory device includes further instructions that, when executed by the processor, cause the processor to:
 compare one or both of the instantaneous tidal volume and the instantaneous respiration rate to a pre-determined threshold and, if one or both of the values exceeds a respective predetermined limit, generate an alert to the caregiver.   
     
     
         14 . The patient support apparatus of  claim 12 , wherein the memory device includes further instructions that, when executed by the processor, cause the processor, when processing the signals to characterize movement of the patient's center of mass, apply a model for the patient support apparatus including the features of the patient's ballistocardiographic heart rate, the patient weight, and movement of the patient's center of mass in three axes to improve the characterization. 
     
     
         15 . The patient support apparatus of  claim 12 , wherein the memory device includes further instructions that, when executed by the processor, cause the processor, when processing the signals to characterize movement of the patient's center of mass, apply a model for the patient support apparatus including the feature of movement of the patient's rib cage in the dorso-ventral direction to improve the characterization. 
     
     
         16 . The patient support apparatus of  claim 12 , wherein the memory device includes further instructions that, when executed by the processor, cause the processor, when processing the signals to characterize movement of the patient's center of mass, apply a model for the patient support apparatus including the feature of movement of the patient's in the Z axis of the bed to improve the characterization.